As mentioned previously, PCA is a linear dimension reduction technique widely used in machine learning. Using correlations between features, PCA finds the direction of maximum variance in high dimensional data and projects data onto a new subspace of fewer dimension. [1]}
As mentioned previously, we assume that we have a data matrix \(X \in \mathbb {R}^{m\times n}\)  where each of the \(m\)  rows is thought of as a point in \(m\)  dimensional space.
